Annual model - Regions
Tanya Rogers
3/29/2022
library(dplyr)
library(lavaan)
library(DiagrammeR)
library(tidyr)
library(ggplot2)
# For saving SEM diagrams:
library(purrr)
library(DiagrammeRsvg)
library(rsvg)
library(png)
library(grid)
library(ggpubr)Import data
combined=read.csv("data/annual_averages/annual_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1975) %>% arrange(Region,Year)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("West","North","South")
years=1975:2021
focaldata = focaldata %>%
mutate(tzoop=hcope+clad+mysid+pcope+rotif_m,
tzoop_e=hcope_e+clad_e+mysid_e+pcope_e+rotif_e,
hzoop=hcope+clad+rotif_m,
hzoop_e=hcope_e+clad_e+rotif_e,
pzoop=mysid+pcope,
pzoop_e=mysid_e+pcope_e,
turbid=-secchi)
fvars=c(fvars,"tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid")
cnames=rbind(cnames,data.frame(Longname=NA,Shortname=c("tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid"),
Diagramname=c("total zooplankton",
"total zooplankton\nenergy",
"herbivorous\nzooplankton",
"herbivorous\nzooplankton\nenergy",
"predatory\nzooplankton",
"predatory\nzooplankton\nenergy",
"turbidity"),
Datacolumn=NA,Log=c(rep("yes",6),"no"),
Color=c("black","black","#ED7D31","#ED7D31","#7030A0",
"#7030A0","#4472C4")))
#focal variables
varnames=c("temp","flow","turbid","chla","hzoop","pzoop","tzoop","amphi","potam","corbic","estfish","estfish_bsmt","estfish_stn")
source("analysis/myLavaanPlot.r")
source("analysis/semDiagramFunctions.r")Data prep
Log transform, scale
#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
x2=x[which(!is.na(x))]
if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
else {log(x)}
}
focaldatalog = focaldata %>%
mutate(flow=flow-min(flow,na.rm=T)) %>% #get rid of negative flow values
mutate_at(logvars,logtrans)
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:2)]
fdr=fdr0 %>% group_by(region) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame()
#detrended data
fdr_dtr=fdr0 %>% group_by(region) %>%
#detrend
mutate_at(tvars,function(x) {
x<<-x
if(!all(is.na(x))) {
if((length(which(x==0))/length(x))<0.5) {
x2<<-x
x2[x2==0]=NA
res<<-residuals(lm(x2~years))
out=x
out[which(!is.na(x2))]=res
return(out)
} else {return(x)}
} else {return(x)}
}) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame() Time series plots
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(varnames)` instead of `varnames` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Other useful plots
Breakdown of total zooplankton biomass.
## Warning: Removed 15 rows containing missing values (position_stack).
Similarity of fish indices.
## Warning: Removed 7 row(s) containing missing values (geom_path).
## Warning: Removed 7 row(s) containing missing values (geom_path).
Correlation between biomass and energy.
for(i in 1:length(regions)) {
dtemp=filter(fdr,region==regions[i])
print(regions[i])
print(cor(dtemp$tzoop,dtemp$tzoop_e,use = "p"))
print(cor(dtemp$hzoop,dtemp$hzoop_e,use = "p"))
print(cor(dtemp$pzoop,dtemp$pzoop_e,use = "p"))
}## [1] "North"
## [,1]
## [1,] 0.9989681
## [,1]
## [1,] 0.9958644
## [,1]
## [1,] 0.9990414
## [1] "South"
## [,1]
## [1,] 0.9987488
## [,1]
## [1,] 0.9977845
## [,1]
## [1,] 0.9993711
## [1] "West"
## [,1]
## [1,] 0.999451
## [,1]
## [1,] 0.9986605
## [,1]
## [1,] 0.9994171Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
Note correlation of fish indices, or lack thereof.
SEM model
With and without detrending.
West
#1
# modwest='zoop=~hcope+mysid
# fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# fish~zoop+flow
# '
#2
# modwest='chla~potam+flow
# tzoop~chla+potam+flow
# estfish_bsmt~tzoop+flow
# estfish_bsot~tzoop+flow
# '
#3
# modwest='chla~potam+flow+temp+turbid
# tzoop~chla+potam+flow+temp+turbid
# amphi~chla+potam+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow
# #estfish_bsot~tzoop+amphi+flow+temp+turbid
# amphi~~tzoop
# '
#4
# modwest='chla~potam+flow+temp+turbid
# hzoop~chla+potam+flow+temp+turbid
# pzoop~chla+potam+flow+temp+turbid+hzoop
# amphi~chla+potam+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid
# amphi~~hzoop+pzoop
# '
#5
modwest='chla~potam+flow+temp+turbid
hzoop~chla+potam+flow+temp+turbid
pzoop~chla+potam+flow+temp+turbid+hzoop
fish~hzoop+pzoop+potam+flow+temp+turbid
fish=~estfish+estfish_stn+estfish_bsmt
'
modfitwest=sem(modwest, data=filter(fdr,region=="West"))
modfitwest_dtr=sem(modwest, data=filter(fdr_dtr,region=="West"))
summary(modfitwest, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 21.159
## Degrees of freedom 15
## P-value (Chi-square) 0.132
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.875 0.879
## estfish_stn 0.919 0.115 8.016 0.000 0.804 0.883
## estfish_bsmt 0.848 0.143 5.943 0.000 0.742 0.752
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.285 0.169 -1.688 0.091 -0.285 -0.285
## flow 0.281 0.182 1.540 0.124 0.281 0.280
## temp -0.281 0.150 -1.870 0.062 -0.281 -0.278
## turbid -0.221 0.199 -1.112 0.266 -0.221 -0.213
## hzoop ~
## chla 0.520 0.103 5.067 0.000 0.520 0.599
## potam -0.277 0.114 -2.439 0.015 -0.277 -0.319
## flow 0.137 0.122 1.120 0.263 0.137 0.157
## temp 0.025 0.102 0.245 0.807 0.025 0.028
## turbid -0.220 0.131 -1.682 0.093 -0.220 -0.245
## pzoop ~
## chla 0.397 0.084 4.727 0.000 0.397 0.455
## potam -0.034 0.078 -0.437 0.662 -0.034 -0.039
## flow -0.344 0.079 -4.351 0.000 -0.344 -0.392
## temp -0.005 0.065 -0.075 0.940 -0.005 -0.006
## turbid 0.109 0.087 1.262 0.207 0.109 0.121
## hzoop 0.594 0.101 5.889 0.000 0.594 0.591
## fish ~
## hzoop 0.214 0.157 1.364 0.173 0.244 0.212
## pzoop 0.299 0.145 2.068 0.039 0.342 0.298
## potam -0.301 0.090 -3.332 0.001 -0.343 -0.343
## flow 0.195 0.101 1.928 0.054 0.222 0.221
## temp 0.010 0.072 0.139 0.890 0.011 0.011
## turbid 0.221 0.101 2.202 0.028 0.253 0.244
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.225 0.064 3.517 0.000 0.225 0.227
## .estfish_stn 0.182 0.053 3.469 0.001 0.182 0.220
## .estfish_bsmt 0.424 0.102 4.147 0.000 0.424 0.435
## .chla 0.718 0.161 4.472 0.000 0.718 0.719
## .hzoop 0.303 0.068 4.472 0.000 0.303 0.402
## .pzoop 0.123 0.028 4.472 0.000 0.123 0.162
## .fish 0.065 0.040 1.624 0.104 0.085 0.085
##
## R-Square:
## Estimate
## estfish 0.773
## estfish_stn 0.780
## estfish_bsmt 0.565
## chla 0.281
## hzoop 0.598
## pzoop 0.838
## fish 0.915summary(modfitwest_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 15.035
## Degrees of freedom 15
## P-value (Chi-square) 0.449
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.648 0.702
## estfish_stn 1.193 0.249 4.787 0.000 0.773 0.784
## estfish_bsmt 0.989 0.249 3.972 0.000 0.640 0.648
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam 0.175 0.157 1.119 0.263 0.175 0.178
## flow 0.632 0.196 3.228 0.001 0.632 0.629
## temp -0.330 0.144 -2.295 0.022 -0.330 -0.325
## turbid -0.422 0.183 -2.307 0.021 -0.422 -0.418
## hzoop ~
## chla 0.465 0.133 3.497 0.000 0.465 0.465
## potam -0.013 0.134 -0.099 0.921 -0.013 -0.013
## flow 0.466 0.185 2.521 0.012 0.466 0.464
## temp -0.026 0.128 -0.200 0.842 -0.026 -0.025
## turbid -0.460 0.164 -2.809 0.005 -0.460 -0.455
## pzoop ~
## chla 0.431 0.095 4.531 0.000 0.431 0.511
## potam 0.019 0.084 0.230 0.818 0.019 0.023
## flow -0.269 0.124 -2.161 0.031 -0.269 -0.318
## temp -0.033 0.080 -0.406 0.685 -0.033 -0.038
## turbid -0.004 0.112 -0.033 0.973 -0.004 -0.004
## hzoop 0.418 0.099 4.223 0.000 0.418 0.495
## fish ~
## hzoop 0.039 0.105 0.371 0.710 0.060 0.060
## pzoop 0.227 0.118 1.934 0.053 0.351 0.295
## potam -0.085 0.074 -1.147 0.251 -0.132 -0.133
## flow 0.524 0.131 3.998 0.000 0.809 0.803
## temp -0.041 0.070 -0.587 0.557 -0.063 -0.062
## turbid -0.018 0.099 -0.179 0.858 -0.027 -0.027
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.431 0.106 4.073 0.000 0.431 0.507
## .estfish_stn 0.373 0.102 3.652 0.000 0.373 0.385
## .estfish_bsmt 0.565 0.134 4.209 0.000 0.565 0.580
## .chla 0.698 0.156 4.472 0.000 0.698 0.703
## .hzoop 0.494 0.110 4.472 0.000 0.494 0.497
## .pzoop 0.193 0.043 4.472 0.000 0.193 0.274
## .fish 0.023 0.041 0.563 0.573 0.055 0.055
##
## R-Square:
## Estimate
## estfish 0.493
## estfish_stn 0.615
## estfish_bsmt 0.420
## chla 0.297
## hzoop 0.503
## pzoop 0.726
## fish 0.945labelswest <- createLabels(modfitwest, cnames)
# residuals(modfitwest)
# modificationindices(modfitwest)North
#1
# modnorth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
# modnorth='zoop=~clad
# zoop~chla+corbic+potam+flow
# chla~corbic+potam+flow
# estfish_bsmt~zoop+flow+sside+chla
# estfish_bsot~zoop+flow+sside+chla
# '
#2
# modnorth='chla~corbic+potam+flow
# tzoop~chla+corbic+potam+flow
# estfish_bsmt~tzoop+flow+chla
# estfish_bsot~tzoop+flow
# '
#3
# modnorth='chla~corbic+potam+flow+temp+turbid
# tzoop~chla+corbic+potam+flow+temp+turbid
# amphi~chla+corbic+potam+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow+temp+turbid+chla+sside
# #estfish_bsot~tzoop+amphi+flow+temp+turbid+sside
# amphi~~tzoop
# '
#4
# modnorth='chla~corbic+flow+temp+turbid
# hzoop~chla+corbic+flow+temp+turbid
# pzoop~chla+corbic+flow+temp+turbid+hzoop
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid+chla+sside
# amphi~~hzoop+pzoop
# '
#5
modnorth='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~chla+hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish+estfish_stn #+estfish_bsmt
estfish_stn~~chla
'
modfitnorth=sem(modnorth, data=filter(fdr,region=="North"))
modfitnorth_dtr=sem(modnorth, data=filter(fdr_dtr,region=="North"))
summary(modfitnorth, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 4.919
## Degrees of freedom 5
## P-value (Chi-square) 0.426
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.833 0.812
## estfish_stn 0.892 0.171 5.207 0.000 0.743 0.839
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.449 0.126 3.549 0.000 0.449 0.450
## flow 0.211 0.146 1.441 0.150 0.211 0.201
## temp 0.182 0.131 1.392 0.164 0.182 0.178
## turbid 0.106 0.134 0.793 0.428 0.106 0.103
## hzoop ~
## chla 0.131 0.120 1.095 0.273 0.131 0.152
## corbic 0.316 0.120 2.628 0.009 0.316 0.367
## flow -0.054 0.127 -0.424 0.672 -0.054 -0.060
## temp 0.033 0.113 0.288 0.773 0.033 0.037
## turbid 0.402 0.108 3.713 0.000 0.402 0.454
## pzoop ~
## chla 0.580 0.089 6.508 0.000 0.580 0.602
## corbic 0.346 0.095 3.635 0.000 0.346 0.360
## flow -0.496 0.093 -5.332 0.000 -0.496 -0.492
## temp 0.003 0.083 0.031 0.975 0.003 0.003
## turbid 0.122 0.092 1.335 0.182 0.122 0.124
## hzoop 0.160 0.114 1.397 0.162 0.160 0.143
## fish ~
## chla -0.554 0.187 -2.965 0.003 -0.666 -0.675
## hzoop 0.460 0.157 2.925 0.003 0.553 0.483
## pzoop 0.526 0.207 2.541 0.011 0.631 0.617
## corbic -0.266 0.153 -1.743 0.081 -0.320 -0.325
## flow 0.232 0.161 1.443 0.149 0.279 0.271
## temp 0.104 0.112 0.924 0.356 0.124 0.123
## turbid 0.285 0.125 2.281 0.023 0.342 0.338
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.280 0.113 2.477 0.013 0.280 0.589
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.357 0.115 3.096 0.002 0.357 0.340
## .estfish_stn 0.321 0.109 2.955 0.003 0.321 0.410
## .chla 0.706 0.156 4.528 0.000 0.706 0.686
## .hzoop 0.417 0.092 4.528 0.000 0.417 0.545
## .pzoop 0.224 0.049 4.528 0.000 0.224 0.234
## .fish 0.210 0.096 2.181 0.029 0.302 0.302
##
## R-Square:
## Estimate
## estfish 0.660
## estfish_stn 0.590
## chla 0.314
## hzoop 0.455
## pzoop 0.766
## fish 0.698summary(modfitnorth_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 5.852
## Degrees of freedom 5
## P-value (Chi-square) 0.321
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.608 0.613
## estfish_stn 1.198 0.430 2.784 0.005 0.729 0.729
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.515 0.158 3.258 0.001 0.515 0.485
## flow 0.151 0.160 0.946 0.344 0.151 0.144
## temp 0.173 0.134 1.290 0.197 0.173 0.167
## turbid 0.200 0.142 1.412 0.158 0.200 0.193
## hzoop ~
## chla 0.342 0.166 2.063 0.039 0.342 0.350
## corbic -0.027 0.196 -0.138 0.890 -0.027 -0.026
## flow 0.151 0.190 0.795 0.426 0.151 0.146
## temp 0.091 0.159 0.569 0.569 0.091 0.089
## turbid -0.008 0.170 -0.046 0.963 -0.008 -0.008
## pzoop ~
## chla 0.745 0.107 6.955 0.000 0.745 0.725
## corbic 0.292 0.121 2.418 0.016 0.292 0.267
## flow -0.544 0.117 -4.637 0.000 -0.544 -0.501
## temp 0.019 0.098 0.198 0.843 0.019 0.018
## turbid 0.006 0.105 0.056 0.955 0.006 0.006
## hzoop 0.037 0.096 0.382 0.703 0.037 0.035
## fish ~
## chla -0.411 0.215 -1.911 0.056 -0.676 -0.692
## hzoop 0.073 0.101 0.724 0.469 0.120 0.120
## pzoop 0.328 0.182 1.800 0.072 0.539 0.568
## corbic -0.412 0.190 -2.172 0.030 -0.678 -0.653
## flow 0.326 0.177 1.840 0.066 0.537 0.521
## temp 0.144 0.118 1.227 0.220 0.237 0.234
## turbid 0.046 0.123 0.377 0.706 0.076 0.075
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.461 0.185 2.486 0.013 0.461 0.679
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.614 0.170 3.609 0.000 0.614 0.624
## .estfish_stn 0.620 0.242 2.560 0.010 0.620 0.621
## .chla 0.743 0.164 4.528 0.000 0.743 0.708
## .hzoop 0.839 0.185 4.528 0.000 0.839 0.835
## .pzoop 0.317 0.070 4.528 0.000 0.317 0.286
## .fish 0.167 0.109 1.533 0.125 0.453 0.453
##
## R-Square:
## Estimate
## estfish 0.376
## estfish_stn 0.379
## chla 0.292
## hzoop 0.165
## pzoop 0.714
## fish 0.547labelsnorth <- createLabels(modfitnorth, cnames)
# residuals(modfitnorth)
# modificationindices(modfitnorth)South
#1
# modsouth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+corbic+flow
# chla~corbic+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
#2
# modsouth='chla~corbic+flow
# tzoop~chla+corbic+flow
# estfish_bsmt~tzoop+flow+corbic+sside
# estfish_bsot~tzoop+flow+corbic+sside
# '
#3
# modsouth='chla~corbic+flow+temp+turbid
# tzoop~chla+corbic+flow+temp+turbid
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow+temp+turbid+corbic+sside
# #estfish_bsot~tzoop+amphi+flow+temp+turbid+corbic+sside
# amphi~~tzoop
# '
#4
# modsouth='chla~corbic+flow+temp+turbid
# hzoop~chla+corbic+flow+temp+turbid
# pzoop~chla+corbic+flow+temp+turbid+hzoop
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid+corbic+sside
# amphi~~hzoop+pzoop
# '
#5
modsouth='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish+estfish_stn+estfish_bsmt
'
modsouth_dtr='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish #+estfish_stn+estfish_bsmt
'
modfitsouth=sem(modsouth, data=filter(fdr,region=="South"))
modfitsouth_dtr=sem(modsouth_dtr, data=filter(fdr_dtr,region=="South"))
summary(modfitsouth, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 17.243
## Degrees of freedom 15
## P-value (Chi-square) 0.305
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.912 0.920
## estfish_stn 0.776 0.124 6.236 0.000 0.708 0.763
## estfish_bsmt 0.635 0.152 4.170 0.000 0.579 0.587
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.284 0.149 1.906 0.057 0.284 0.283
## flow -0.124 0.137 -0.906 0.365 -0.124 -0.128
## temp 0.081 0.140 0.578 0.563 0.081 0.081
## turbid 0.392 0.158 2.478 0.013 0.392 0.373
## hzoop ~
## chla 0.454 0.112 4.058 0.000 0.454 0.514
## corbic 0.324 0.110 2.944 0.003 0.324 0.366
## flow -0.015 0.098 -0.150 0.881 -0.015 -0.017
## temp -0.057 0.100 -0.573 0.566 -0.057 -0.065
## turbid -0.028 0.120 -0.236 0.814 -0.028 -0.030
## pzoop ~
## chla 0.510 0.126 4.039 0.000 0.510 0.533
## corbic -0.024 0.115 -0.206 0.837 -0.024 -0.025
## flow -0.078 0.093 -0.841 0.401 -0.078 -0.084
## temp 0.056 0.095 0.588 0.556 0.056 0.058
## turbid 0.180 0.114 1.576 0.115 0.180 0.179
## hzoop 0.271 0.150 1.802 0.072 0.271 0.250
## fish ~
## hzoop 0.230 0.125 1.833 0.067 0.252 0.222
## pzoop -0.095 0.112 -0.849 0.396 -0.104 -0.099
## corbic 0.174 0.097 1.799 0.072 0.191 0.190
## flow -0.080 0.078 -1.021 0.307 -0.088 -0.091
## temp 0.023 0.080 0.284 0.776 0.025 0.025
## turbid 0.761 0.102 7.495 0.000 0.835 0.793
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.152 0.069 2.193 0.028 0.152 0.154
## .estfish_stn 0.360 0.090 3.993 0.000 0.360 0.418
## .estfish_bsmt 0.640 0.149 4.296 0.000 0.640 0.656
## .chla 0.723 0.162 4.472 0.000 0.723 0.724
## .hzoop 0.362 0.081 4.472 0.000 0.362 0.465
## .pzoop 0.327 0.073 4.472 0.000 0.327 0.358
## .fish 0.116 0.066 1.777 0.076 0.140 0.140
##
## R-Square:
## Estimate
## estfish 0.846
## estfish_stn 0.582
## estfish_bsmt 0.344
## chla 0.276
## hzoop 0.535
## pzoop 0.642
## fish 0.860summary(modfitsouth_dtr, standardized=T, rsq=T)## lavaan 0.6-9 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 0.301
## Degrees of freedom 1
## P-value (Chi-square) 0.583
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.909 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.120 0.157 0.764 0.445 0.120 0.120
## flow -0.033 0.162 -0.202 0.840 -0.033 -0.034
## temp 0.176 0.158 1.110 0.267 0.176 0.176
## turbid -0.048 0.163 -0.291 0.771 -0.048 -0.048
## hzoop ~
## chla 0.443 0.125 3.550 0.000 0.443 0.456
## corbic 0.279 0.126 2.212 0.027 0.279 0.288
## flow 0.030 0.130 0.234 0.815 0.030 0.032
## temp -0.016 0.128 -0.129 0.898 -0.016 -0.017
## turbid -0.148 0.131 -1.137 0.256 -0.148 -0.153
## pzoop ~
## chla 0.543 0.137 3.959 0.000 0.543 0.523
## corbic -0.120 0.129 -0.935 0.350 -0.120 -0.116
## flow -0.021 0.125 -0.168 0.867 -0.021 -0.021
## temp 0.162 0.123 1.316 0.188 0.162 0.156
## turbid -0.103 0.128 -0.808 0.419 -0.103 -0.099
## hzoop 0.218 0.150 1.450 0.147 0.218 0.204
## fish ~
## hzoop 0.226 0.157 1.440 0.150 0.248 0.238
## pzoop -0.259 0.141 -1.834 0.067 -0.285 -0.292
## corbic 0.107 0.138 0.778 0.436 0.118 0.117
## flow -0.004 0.132 -0.032 0.975 -0.005 -0.005
## temp 0.120 0.134 0.897 0.370 0.132 0.130
## turbid 0.348 0.136 2.566 0.010 0.383 0.379
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.000 0.000 0.000
## .chla 0.927 0.205 4.528 0.000 0.927 0.950
## .hzoop 0.591 0.131 4.528 0.000 0.591 0.642
## .pzoop 0.547 0.121 4.528 0.000 0.547 0.520
## .fish 0.616 0.136 4.528 0.000 0.747 0.747
##
## R-Square:
## Estimate
## estfish 1.000
## chla 0.050
## hzoop 0.358
## pzoop 0.480
## fish 0.253labelssouth <- createLabels(modfitsouth, cnames)
# residuals(modfitsouth)
# modificationindices(modfitsouth)Nice plots
Original units
West
North
South
Detrended
West
North
South
Save updated SEM diagrams
plot_grobs <- map(list(figW, figN, figS), ~convert_html_to_grob(.x, 2000))
combined_figure <- ggarrange(plotlist=plot_grobs, labels="auto",
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEM (regional)",
color = "black",
face = "bold",
size = 9))# +
#theme(plot.margin = unit(c(1,0,0,0), "lines"))
ggsave('./fig_output/sem_annual_regions.png', combined_figure, width=6, height=6,
dpi=300, bg = "white")Combine and save annual no region model and annual regional models
load('./fig_output/sem_annual_noregions.RData')
plot_grobs <- map(list(sem_annual_noregions, figW, figN, figS),
~convert_html_to_grob(.x, 2000))
combined_figure <- ggarrange(plotlist=plot_grobs, labels="auto",
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEMs",
color = "black",
face = "bold",
size = 11))
ggsave('./fig_output/sem_annual.png', combined_figure, width=8, height=7, dpi=300,
bg = "white")
#detrended
plot_grobs <- map(list(sem_annual_noregions_dtr, figWdt, figNdt, figSdt),
~convert_html_to_grob(.x, 2000))
combined_figure_dtr <- ggarrange(plotlist=plot_grobs, labels="auto",
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEMs, Detrended",
color = "black",
face = "bold",
size = 11))
ggsave('./fig_output/sem_annual_detrend.png', combined_figure_dtr, width=8, height=7,
dpi=300, bg = "white")